Towards Interpretable Face Recognition

Understand

Deep CNNs have been pushing the frontier of visual recognition over past years.

• Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions.
• Recent works further push the interpretability in the network learning stage to learn more meaningful representations.
• In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition.